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A deep learning approach to identify and segment alpha-smooth muscle actin stress fiber positive cells

ABSTRACT: Cardiac fibrosis is a pathological process characterized by excessive tissue deposition, matrix remodeling, and tissue stiffening, which eventually leads to organ failure. On a cellular level, the development of fibrosis is associated with the activation of cardiac fibroblasts into myofibroblasts, a highly contractile and secretory phenotype. Myofibroblasts are commonly identified in vitro by the de novo assembly of alpha-smooth muscle actin stress fibers; however, there are few methods to automate stress fiber identification, which can lead to subjectivity and tedium in the process. To address this limitation, we present a computer vision model to classify and segment cells containing alpha-smooth muscle actin stress fibers into 2 classes (α-SMA SF+ and α-SMA SF-), with a high degree of accuracy (cell accuracy: 77%, F1 score 0.79). The model combines standard image processing methods with deep learning techniques to achieve semantic segmentation of the different cell phenotypes. We apply this model to cardiac fibroblasts cultured on hyaluronic acid-based hydrogels of various moduli to induce alpha-smooth muscle actin stress fiber formation. The model successfully predicts the same trends in stress fiber identification as obtained with a manual analysis. Taken together, this work demonstrates a process to automate stress fiber identification in in vitro fibrotic models, thereby increasing reproducibility in fibroblast phenotypic characterization.

SUBMITTER: Hillsley A

PROVIDER: S-EPMC8575943 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Capillary pericytes express α-smooth muscle actin, which requires prevention of filamentous-actin depolymerization for detection.

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Project description:PurposeThe companion diagnostic test for trastuzumab has not changed much in the last 25 years. We used high-plex digital spatial profiling to identify biomarkers besides HER2 that can help predict response to trastuzumab in HER2-positive breast cancer.Experimental designFifty-eight protein targets were measured in three different molecularly defined compartments by the NanoString GeoMx Digital Spatial Profiler (DSP) in a tissue microarray containing 151 patients with breast cancer that received adjuvant trastuzumab as part of the Hellenic Cooperative Oncology Group 10/05 clinical trial. Promising candidate biomarkers were orthogonally validated with quantitative immunofluorescence (QIF). RNA-sequencing data from the Neoadjuvant Lapatinib and/or Trastuzumab Treatment Optimisation Study (NeoALTTO) were accessed to provide independent cohort validation. Disease-free survival (DFS) was the main outcome assessed. Statistical analyses were performed using a two-sided test (α = 0.05) and multiple testing correction (Benjamini-Hochberg method, FDR < 0.1).ResultsBy DSP, high expression of alpha-smooth muscle actin (α-SMA), both in the leukocyte and stromal compartments, was associated with shorter DFS in univariate analysis (P = 0.002 and P = 0.023, respectively). High α-SMA expression in the stroma was validated by QIF after controlling for estrogen receptor and progesterone receptor status [HR, 3.12; 95% confidence interval (CI), 1.12-8.68; P = 0.029] showing recurrence on trastuzumab in the same cohort. In the NeoALTTO cohort, elevated levels of ACTA2 were predictive for shorter DFS in the multivariate analysis (HR, 3.21; 95% CI, 1.14-9.05; P = 0.027).ConclusionsThis work identifies α-SMA as a novel, easy-to-implement biomarker of resistance to trastuzumab that may be valuable in settings where trastuzumab is combined with other therapies.

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